Lipopolysaccharides (LPS) or endotoxins are outer membrane constituents of gram-negative bacteria that play a key role in the pathogenesis of septic shock, a leading cause of mortality worldwide for which there is as yet no effective therapy. The pathogenesis of certain Category A (Francisella tularensis) and Category B (Brucella spp.) bioterrorism agents also involves deleterious host responses to LPS. One possible approach to developing novel therapeutic strategies to treat sepsis is to sequester circulating LPS, a strategy that has been historically addressed using monoclonal antibodies directed against the structurally conserved lipid regions of LPS. However, a series of clinical trials using monoclonal antibodies have been unsuccessful owing to the lack of accessible recognition sites on the lipid. Our previous work on identifying structural requisites necessary for binding and neutralization of LPS in a variety of proteins, peptides and small molecules led to the identification of a novel class of structurally simple, nontoxic molecules, the lipopolyamines, which bind and neutralize LPS in vitro, and afford protection against LPS challenge in two murine models of gram-negative sepsis. In collaboration with MediQuest Therapeutics, Inc., we propose to synthesize libraries of novel compounds rationally designed to maximize binding affinity and neutralization potency, and to exhibit desirable pharmacokinetic and toxicological profiles, based on optimal structural templates that we have already established with the lipopolyamines. Employing a hierarchical screening strategy, the interactions of these molecules with LPS will be comprehensively evaluated. Test compounds will be screened for the ability to inhibit LPS-induced cellular activation and production of key proinflammatory mediators of septic shock. Highly active molecules will be further tested in two murine models of gram-negative sepsis. The toxicity of the compounds will be systematically determined in a panel of in vitro assays. These studies will serve to generate data for anticipated IND submissions. [unreadable] [unreadable]